




'''simple docstring'''


import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation


def     SCREAMING_SNAKE_CASE							(   a_						:				List[Any]      ):
  __a								=       384
  __a								=       7
  if "tiny" in model_name:
    __a								=       96
    __a								=       (2, 2, 6, 2)
    __a								=       (3, 6, 12, 24)
  elif "small" in model_name:
    __a								=       96
    __a								=       (2, 2, 18, 2)
    __a								=       (3, 6, 12, 24)
  elif "base" in model_name:
    __a								=       128
    __a								=       (2, 2, 18, 2)
    __a								=       (4, 8, 16, 32)
    __a								=       12
    __a								=       512
  elif "large" in model_name:
    __a								=       192
    __a								=       (2, 2, 18, 2)
    __a								=       (6, 12, 24, 48)
    __a								=       12
    __a								=       768

  # set label information
  __a								=       150
  __a								=       'huggingface/label-files'
  __a								=       'ade20k-id2label.json'
  __a								=       json.load(open(hf_hub_download(a_					,       a_					,       repo_type='dataset'      )					,       'r'      )      )
  __a								=       {int(a_      ): v for k, v in idalabel.items()}
  __a								=       {v: k for k, v in idalabel.items()}

  __a								=       SwinConfig(
      embed_dim=a_					,       depths=a_					,       num_heads=a_					,       window_size=a_					,       out_features=['stage1', 'stage2', 'stage3', 'stage4']					,       )
  __a								=       UperNetConfig(
      backbone_config=a_					,       auxiliary_in_channels=a_					,       num_labels=a_					,       idalabel=a_					,       labelaid=a_					,       )

  return config


def     SCREAMING_SNAKE_CASE							(   a_						:				str      ):
  __a								=       []

  # fmt: off
  # stem
  rename_keys.append(('backbone.patch_embed.projection.weight', 'backbone.embeddings.patch_embeddings.projection.weight')      )
  rename_keys.append(('backbone.patch_embed.projection.bias', 'backbone.embeddings.patch_embeddings.projection.bias')      )
  rename_keys.append(('backbone.patch_embed.norm.weight', 'backbone.embeddings.norm.weight')      )
  rename_keys.append(('backbone.patch_embed.norm.bias', 'backbone.embeddings.norm.bias')      )
  # stages
  for i in range(len(config.backbone_config.depths      )      ):
    for j in range(config.backbone_config.depths[i]      ):
      rename_keys.append((f"backbone.stages.{i}.blocks.{j}.norm1.weight", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight")      )
      rename_keys.append((f"backbone.stages.{i}.blocks.{j}.norm1.bias", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias")      )
      rename_keys.append((f"backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table", f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table")      )
      rename_keys.append((f"backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index", f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index")      )
      rename_keys.append((f"backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight", f"backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight")      )
      rename_keys.append((f"backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias", f"backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias")      )
      rename_keys.append((f"backbone.stages.{i}.blocks.{j}.norm2.weight", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight")      )
      rename_keys.append((f"backbone.stages.{i}.blocks.{j}.norm2.bias", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias")      )
      rename_keys.append((f"backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight", f"backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight")      )
      rename_keys.append((f"backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias", f"backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias")      )
      rename_keys.append((f"backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight", f"backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight")      )
      rename_keys.append((f"backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias", f"backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias")      )

    if i < 3:
      rename_keys.append((f"backbone.stages.{i}.downsample.reduction.weight", f"backbone.encoder.layers.{i}.downsample.reduction.weight")      )
      rename_keys.append((f"backbone.stages.{i}.downsample.norm.weight", f"backbone.encoder.layers.{i}.downsample.norm.weight")      )
      rename_keys.append((f"backbone.stages.{i}.downsample.norm.bias", f"backbone.encoder.layers.{i}.downsample.norm.bias")      )
    rename_keys.append((f"backbone.norm{i}.weight", f"backbone.hidden_states_norms.stage{i+1}.weight")      )
    rename_keys.append((f"backbone.norm{i}.bias", f"backbone.hidden_states_norms.stage{i+1}.bias")      )

  # decode head
  rename_keys.extend(
      [
          ('decode_head.conv_seg.weight', 'decode_head.classifier.weight'),
          ('decode_head.conv_seg.bias', 'decode_head.classifier.bias'),
          ('auxiliary_head.conv_seg.weight', 'auxiliary_head.classifier.weight'),
          ('auxiliary_head.conv_seg.bias', 'auxiliary_head.classifier.bias'),
      ]      )
  # fmt: on

  return rename_keys


def     SCREAMING_SNAKE_CASE							(   a_						:				str					,       a_						:				Optional[int]					,       a_						:				Optional[Any]      ):
  __a								=       dct.pop(a_      )
  __a								=       val


def     SCREAMING_SNAKE_CASE							(   a_						:				Any					,       a_						:				str      ):
  __a								=       [int(backbone_config.embed_dim * 2**i      ) for i in range(len(backbone_config.depths      )      )]
  for i in range(len(backbone_config.depths      )      ):
    __a								=       num_features[i]
    for j in range(backbone_config.depths[i]      ):
      # fmt: off
      # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
      __a								=       state_dict.pop(f"backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight"      )
      __a								=       state_dict.pop(f"backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.bias"      )
      # next, add query, keys and values (in that order) to the state dict
      __a								=       in_proj_weight[:dim, :]
      __a								=       in_proj_bias[: dim]
      __a								=       in_proj_weight[
          dim : dim * 2, :
      ]
      __a								=       in_proj_bias[
          dim : dim * 2
      ]
      __a								=       in_proj_weight[
          -dim :, :
      ]
      __a								=       in_proj_bias[-dim :]
      # fmt: on


def     SCREAMING_SNAKE_CASE							(   a_						:				List[str]      ):
  __a						,							__a								=       x.shape
  __a								=       x.reshape(a_					,       4					,       in_channel // 4      )
  __a								=       x[:, [0, 2, 1, 3], :].transpose(1					,       2      ).reshape(a_					,       a_      )
  return x


def     SCREAMING_SNAKE_CASE							(   a_						:				List[Any]      ):
  __a						,							__a								=       x.shape
  __a								=       x.reshape(a_					,       in_channel // 4					,       4      )
  __a								=       x[:, :, [0, 2, 1, 3]].transpose(1					,       2      ).reshape(a_					,       a_      )

  return x


def     SCREAMING_SNAKE_CASE							(   a_						:				Tuple      ):
  __a								=       x.shape[0]
  __a								=       x.reshape(4					,       in_channel // 4      )
  __a								=       x[[0, 2, 1, 3], :].transpose(0					,       1      ).reshape(a_      )
  return x


def     SCREAMING_SNAKE_CASE							(   a_						:				Union[str, Any]      ):
  __a								=       x.shape[0]
  __a								=       x.reshape(in_channel // 4					,       4      )
  __a								=       x[:, [0, 2, 1, 3]].transpose(0					,       1      ).reshape(a_      )
  return x


def     SCREAMING_SNAKE_CASE							(   a_						:				Union[str, Any]					,       a_						:				Tuple					,       a_						:				Optional[int]      ):
  __a								=       {
      'upernet-swin-tiny': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth',
      'upernet-swin-small': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth',
      'upernet-swin-base': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth',
      'upernet-swin-large': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth',
  }
  __a								=       model_name_to_url[model_name]
  __a								=       torch.hub.load_state_dict_from_url(a_					,       map_location='cpu'					,       file_name=a_      )[
      'state_dict'
  ]

  for name, param in state_dict.items():
    print(a_					,       param.shape      )

  __a								=       get_upernet_config(a_      )
  __a								=       UperNetForSemanticSegmentation(a_      )
  model.eval()

  # replace "bn" => "batch_norm"
  for key in state_dict.copy().keys():
    __a								=       state_dict.pop(a_      )
    if "bn" in key:
      __a								=       key.replace('bn'					,       'batch_norm'      )
    __a								=       val

  # rename keys
  __a								=       create_rename_keys(a_      )
  for src, dest in rename_keys:
    rename_key(a_					,       a_					,       a_      )
  read_in_q_k_v(a_					,       config.backbone_config      )

  # fix downsample parameters
  for key, value in state_dict.items():
    if "downsample" in key:
      if "reduction" in key:
        __a								=       reverse_correct_unfold_reduction_order(a_      )
      if "norm" in key:
        __a								=       reverse_correct_unfold_norm_order(a_      )

  model.load_state_dict(a_      )

  # verify on image
  __a								=       'https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'
  __a								=       Image.open(requests.get(a_					,       stream=a_      ).raw      ).convert('RGB'      )

  __a								=       SegformerImageProcessor()
  __a								=       processor(a_					,       return_tensors='pt'      ).pixel_values

  with torch.no_grad():
    __a								=       model(a_      )
    __a								=       outputs.logits

  print(logits.shape      )
  print('First values of logits:'					,       logits[0, 0, :3, :3]      )
  # assert values
  if model_name == "upernet-swin-tiny":
    __a								=       torch.tensor(
        [[-7.59_58, -7.59_58, -7.43_02], [-7.59_58, -7.59_58, -7.43_02], [-7.47_97, -7.47_97, -7.30_68]]      )
  elif model_name == "upernet-swin-small":
    __a								=       torch.tensor(
        [[-7.19_21, -7.19_21, -6.95_32], [-7.19_21, -7.19_21, -6.95_32], [-7.09_08, -7.09_08, -6.85_34]]      )
  elif model_name == "upernet-swin-base":
    __a								=       torch.tensor(
        [[-6.58_51, -6.58_51, -6.43_30], [-6.58_51, -6.58_51, -6.43_30], [-6.47_63, -6.47_63, -6.32_54]]      )
  elif model_name == "upernet-swin-large":
    __a								=       torch.tensor(
        [[-7.52_97, -7.52_97, -7.38_02], [-7.52_97, -7.52_97, -7.38_02], [-7.40_44, -7.40_44, -7.25_86]]      )
  print('Logits:'					,       outputs.logits[0, 0, :3, :3]      )
  assert torch.allclose(outputs.logits[0, 0, :3, :3]					,       a_					,       atol=1e-4      )
  print('Looks ok!'      )

  if pytorch_dump_folder_path is not None:
    print(f"Saving model {model_name} to {pytorch_dump_folder_path}"      )
    model.save_pretrained(a_      )
    print(f"Saving processor to {pytorch_dump_folder_path}"      )
    processor.save_pretrained(a_      )

  if push_to_hub:
    print(f"Pushing model and processor for {model_name} to hub"      )
    model.push_to_hub(f"openmmlab/{model_name}"      )
    processor.push_to_hub(f"openmmlab/{model_name}"      )


if __name__ == "__main__":
  UpperCAmelCase_					=   argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--model_name",
      default="upernet-swin-tiny",
      type=str,
      choices=[f"""upernet-swin-{size}""" for size in ["tiny", "small", "base", "large"]],
      help="Name of the Swin + UperNet model you'd like to convert.",
  )
  parser.add_argument(
      "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
  )
  parser.add_argument(
      "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
  )

  UpperCAmelCase_					=   parser.parse_args()
  convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









'''simple docstring'''


import math


def     SCREAMING_SNAKE_CASE							(   a_						:				float					,       a_						:				float      ):

  if initial_intensity < 0:
    raise ValueError('The value of intensity cannot be negative'      )
    # handling of negative values of initial intensity
  if angle < 0 or angle > 360:
    raise ValueError('In Malus Law, the angle is in the range 0-360 degrees'      )
    # handling of values out of allowed range
  return initial_intensity * (math.cos(math.radians(a_      )      ) ** 2)


if __name__ == "__main__":
  import doctest

  doctest.testmod(name="malus_law")



